Graph Algorithms and Theory Laboratory
Head: Prof. Martin C. Golumbic
This laboratory carries out research in the areas of graph theory, algorithms and theory of computation. Graph theory is a sub-field of combinatorics, a relatively new discipline, developed extensively within the past fifty years, which deals with finite structures and the relationship between their elements. Graph theory serves as a theoretical basis for computer science.
Theoretical computer science involves research on the complexity of algorithms and various models of computation. This includes the design of efficient algorithms, parallel models, combinatorial optimization and randomized algorithms. Two weekly seminar series which meet throughout the academic year are sponsored by the laboratory.
The laboratory is headed by Prof. Martin C. Golumbic and includes: Dr. Andrei Asinowski, Dr. Irith Ben-Arroyo Hartman, Dr. Eli Berger, Dr. Marina Lipshteyn, Dr. Gila Morgenstern; Prof. Ilan Newman, read more >>
The Computational Linguistics Group at the Department of Computer Science, University of Haifa, is involved in research and teaching in diverse areas of computational linguistics and natural language processing. Of primary interest are investigations related to Hebrew and other Semitic languages.
Current research interests of the Group include:
- Formal grammars, particularly unification-based linguistic formalisms
- Computational morphology and syntax of Hebrew (and other Semitic languages)
- Language resources for Hebrew
- Machine translation, particularly into morphologically-complex languages
- Computational invetigation of translationese
- Computational and formal approaches to child language acquisition read more >>
Computational Linguistics Laboratory
Head: Prof. Shuly Wintner
The Laboratory for Innovations in Rehabilitation Technology (LIRT)
Head: Prof. Patrice L. (Tamar) Weiss
The Laboratory for Innovations in Rehabilitation Technology (LIRT) at the University of Haifa focuses on development and evaluation of novel virtual environments, haptic interfaces, co-located and online technologies to explore the effect of individual and collaborative rehabilitation. Rehabilitation and special education populations of interest include stroke, spinal cord injury, cerebral palsy, developmental coordination disorder, autism and head trauma. Research has been funded by the European Union, the Israel Science Foundation, the Israel Ministry of Science and Technology and the Israeli Center of Research Excellence (I-CORE): Learning in a NetworKed Society as well as many foundation grants. For the past 10 years LIRT has collaborated with a team of researchers from Italy to enhance social interaction through story-telling and collaborative games among high-functioning children with autism and to evaluate novel collocated technologies to facilitate conflict escalation and de-escalation between Israeli-Jewish and Palestinian-Arab youth wherein face-to- face, tangible individual contributions were combined with joint actions. Most recently, this work has examined the ways in which collaborative technologies can reduce conflict between migrant and host citizens. LIRT also examines ways in which simulations of complex functional environments can enhance the rehabilitation process of healthy and afflicted older adults.
The field of neurocomputation is concerned with the possibility of computation in computers by following the paradigm and analysis of computation that occurs in neurons and the brain. In recent years this has resulted in breakthroughs in pattern recognition, machine learning theory, clustering, associative memory and fault tolerant computation.
Consequently, the precision resulting from the computational and mathematical viewpoint has led to insights helping to clarify aspects of one of the ultimate human research endeavors: understanding the manner in which human thought emerges from the organization of the human brain.
In our laboratory, we focus on three main objectives:
- Isolating new techniques concerned with computation and storage of spatio-temporal patterns
- Modeling psychological theories of human cognitive behavior
- Applying and developing novel techniques, especially feature selection and machine learning tools towards automated read more >>
Head: Prof. Larry M. Manevitz
In recent years much work has been invested into developing computer algorithms to facilitate researches in the field of molecular biology. A large focus has been on string algorithms, since the data for molecular genetics is naturally represented as sequences of characters.
Multiple repeat occur frequently in both DNA and protein sequences. Some multiple repeats have been associated with human genetic diseases.
Another important application for finding multiple repeats in biological sequences is related to the multiple sequence alignment. Producing multiple alignments becomes very complicated when the sequences to be aligned contain multiple repeat, because matches may be present in numerous places. As a precursor to multiple alignment, it is helpful to recognize all multiple repeat within the set of strings that must be aligned. Read more >>
The Software Architecture Laboratory promotes and practices collaborative industry-academia research in the areas of software architecture, software engineering processes and knowledge management in software development. More information regarding some of our ongoing projects may be found in the publications below. Read more >>
The BioMedical Informatics Laboratory at the Department of Information Systems, University of Haifa, researches knowledge-based clinical decision support, an area of artificial intelligence applied to medicine. The two main foci include clinical-guideline-based decision-support systems (CDSS) and systems biology.
Current research interests of the Group include:
- Knowledge representation and modeling of biomedical systems
- Decision-support systems (DSS), in particular clinical-guideline based DSS
- Ontologies and semantic web representations (such as OWL)
- Knowledge and data integration
- Using machine learning for diagnosis and prognosis
- Improving machine learning methods using domain ontologies
- Workflow and Petri Net models for reasoning with biological processes
The clinical and societal challenges that we are currently researching target:
- Using mobile sensors and apps to increase patients’ compliance to therapy
- Personalization of decision-support to individual patients
- Supporting care management of complex patients with multimorbidity
- Crowd-sourcing- based patient-reported outcomes Read more >>
We do research on a wide variety of topics focusing on various areas in Human-Computer Interaction and Information Visualization. Our research focuses on the design, implementation and evaluation of novel technologies, and well as on the study and understanding of how technologies affect human behavior. The lab is part of the Information Systems department at the University of Haifa, and resides and is affiliated with the Caesarea Rothschild Institute (CRI). Read more >>
Machine leaning and Deep Learning (DL) in particular showed very impressive results in various domains, such as image/video, speech, text, etc., showing human level performance in some of them. The progress in image understanding has led to significant improvements in robotics, self-driving cars, gaming, self-service supermarkets, natural interfaces, etc.
However, there are still a number of open problems that prevent DL from being used in practice. One of these problems is the requirement of very large labeled training sets. The performance of DL drops rapidly when the training set size decreases. The requirement of large labeled sets is expensive in terms of data collection and training time. In practice, many learning problems require rapid inference from small amounts of data. In particular, practical systems should be able to recognize a new category from a handful of training images. Moreover, it is easy for a human to recognize novel images of an unseen object after seeing only a single representative of that object. Currently, machines do very poorly in recognizing a novel category with a single training image. We aim to develop machine learning algorithms that can transfer knowledge the way people do and learn fast from a small number of samples.
The other focus of our research is the reliability (security) of machine learning systems. Recent research showed that it is easy to fool machine learning models with crafted inputs that are indistinguishable from the original inputs to a human observer. Such inputs are called adversarial examples. Adversarial example can be created in almost any domain, putting at risk the users of the machine learning systems. Making systems robust to adversarial examples is still an open problem.
To summarize, our lab focuses on three main objectives:
1. One-shot learning and learning from a small number of training examples
2. Reliability and security of the machine learning system
3. Practical applications of deep learning in data science.
The Robotics and Big Data (RBD) Laboratory
Head: Dr. Dan Feldman
The Robotics and Big Data (RBD) Laboratory established in 2014 by Dan Feldman at the Computer Science Department of the University of Haifa. The lab is focused in designing and implementing novel data reduction algorithms (e.g. Core-sets) for learning "Big data" sets in real-time. The data is usually collected from sensors on the robots, reduced on the mini ("Internet of Things") boards, and then being sent to the machines on the cloud that run existing algorithms on the reduced data.
For the robots, we focus on quadcopters, helicopters and cars that are very low-cost and very safe to use inside the apartment, class or building. The sensors include 3-D cameras, 1gram RGB cameras, EEG ("brain readers"), and IMU (gyroscope).
The algorithms are based on novel techniques in machine learning, computational geometry, compressed sensing, and computer vision. read more >>
SCAN: Big-data lab for the research of Social Content & Networks
Head: Dr. Osnat (Ossi) Mokryn
email: Ossimo at gmail.com
At our lab we develop and employ models to research online human footprints. We study big data gathered from user generated content (online reviews and other social media) and evolving social and complex networks, utilizing machine learning, statistical, quantitative, and visualization tools.
This is an era of a User Generated Content (UGC) revolution. People are voicing and sharing opinions and experiences. In my lab, we research this collective voice of the crowd and study their experience, mood, and choices. We model and create digital emotional signatures of experiences; personalized digital textual signatures of the contributors; and activity maps for sites.
The structure and dynamics of interactions between people have always fascinated me. How do they change and evolve in time? React to external shocks, or events? Understanding the mechanisms that govern interactions is fundamental to our ability to predict and interpret our social, political, and economical organizations.
Our lab is sponsored by the Israel Science Foundation (ISF), The Israeli ministry of Science and Technology, and several industry partners. We employ a committed group of researchers, pursuing their PhD and Master degrees, and have openings for full time post-doc and doctoral students.